Virtual path bandwidth allocation in multiuser networks
IEEE/ACM Transactions on Networking (TON)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Towards Truly Agent-Based Traffic and Mobility Simulations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Distributed evaluation functions for fault tolerant multi-rover systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
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Multiagent coordination algorithms provide unique insights into the challenging problem of alleviating traffic congestion. What is particularly interesting in this class of problem is that no individual action (e.g., leave at a given time) is intrinsically "bad" but that combinations of actions among agents lead to undesirable outcomes. As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. In general, the traffic problem can be approached from two distinct perspectives: (i) from a city manager's point of view, where the aim is to optimize a city wide objective function (e.g., minimize total city wide delays), and (ii) from the individual driver's point of view, where each driver is aiming to optimize a personal objective function (e.g., a "timeliness" function that minimizes the difference desired and actual arrival times at a destination). In many cases, these two objective functions are at odds with one another, where drivers aiming to optimize their own objectives yield to congestion and poor values of city objective functions. In this paper we present an objective shaping approach to both types of problems and study the system behavior that arises from the drivers' choices. We first show a topdown approach that provides incentives to drivers and leads to good values of the city manager's objective function. We then present a bottom-up approach that shows that drivers aiming to optimize their own personal timeliness objective lead to poor performance with respect to a city manager's objective function. Finally, we present the intriguing result that drivers that aim to optimize a modified version of their own timeliness function not only perform well in terms of the city manager's objective function, but also perform better with respect to their own original timeliness functions.